Robert RappandClaude Opus 5.5
862825fa1e
ci / test (push) Failing after 22s
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
100 lines
4.4 KiB
YAML
100 lines
4.4 KiB
YAML
# Rollen "gpu-embed" und "gpu-small" (GPU-VMs mit Quadro P2000 + M4000): llama.cpp-Server (Task 6.1).
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# Maxwell (sm_52) und Pascal (sm_61) brauchen CUDA 12.x – daher eigener Build mit festen Architekturen,
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# derselbe Stand wie im Benchmark (ADR-0006): b6000 = Commit 4762ad7, CUDA 12.4.0, sm_52;61.
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# Profile: embed (Embedding + Rerank, gpu-pve44/46), chat (verifier-small/signature-extractor, gpu-pve48).
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# Ausgerollt von der Ansible-Rolle gpu-llamacpp (.env mit COMPOSE_PROFILES und Karten-UUIDs).
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#
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# Kartenwahl: je Dienst device_ids (UUID oder Index aus `nvidia-smi -L`). Ohne Angabe Index 1 = P2000
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# (in gpu-pve44/46/48 ist Index 0 die M4000, sie bleibt frei). `count: 1` griffe die M4000.
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# Netz: Host-Netz wie nex-speech. Veröffentlichte Docker-Ports liefen über DNAT und umgingen ufw;
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# so gelten spätere ufw-Regeln der VM (Task 4.4).
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# Datenschutz: kein --verbose (der loggt Prompts), /slots aus (zeigt Prompt-Inhalte), keine Web-Oberfläche.
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name: knaster-gpu-llamacpp
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x-logging: &logging
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driver: local
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options: {max-size: "5m", max-file: "2"}
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x-llamacpp: &llamacpp
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build:
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context: https://github.com/ggml-org/llama.cpp.git#${LLAMACPP_COMMIT:-4762ad7316dcdec20016ab5985fb46a27902204d}
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dockerfile: .devops/cuda.Dockerfile
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target: server
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args:
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CUDA_VERSION: ${CUDA_VERSION:-12.4.0}
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CUDA_DOCKER_ARCH: ${CUDA_DOCKER_ARCH:-52;61}
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image: knaster/llamacpp-server:${LLAMACPP_REF:-b6000}-cu${CUDA_VERSION:-12.4.0}
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restart: unless-stopped
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network_mode: host
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read_only: true
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tmpfs: [/tmp:size=64m]
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labels: {autoheal: "true"}
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volumes:
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- ${MODELS_DIR:-/srv/models}:/models:ro
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logging: *logging
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services:
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llamacpp-embed:
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<<: *llamacpp
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container_name: llamacpp-embed
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profiles: ["embed"]
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command: ["-m", "/models/${EMBED_GGUF:-Qwen3-Embedding-0.6B-Q8_0.gguf}", "--embedding",
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"--pooling", "${EMBED_POOLING:-last}", "-c", "8192", "-b", "4096", "-ub", "4096", "-np", "1", # ub 8192 sprengt die P2000 (Task 6.1)
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"-ngl", "99", "--alias", "embed-default", "--metrics", "--no-slots", "--no-webui",
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"--host", "0.0.0.0", "--port", "8081"]
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deploy:
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resources:
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reservations:
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devices: [{driver: nvidia, device_ids: ["${EMBED_GPU:-1}"], capabilities: [gpu]}]
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healthcheck: &hc
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test: ["CMD", "curl", "-fsS", "http://127.0.0.1:8081/health"]
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interval: 30s
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timeout: 5s
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retries: 3
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start_period: 120s
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llamacpp-rerank:
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<<: *llamacpp
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container_name: llamacpp-rerank
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profiles: ["embed"]
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command: ["-m", "/models/${RERANK_GGUF:-bge-reranker-v2-m3-Q8_0.gguf}", "--reranking",
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"-c", "8192", "-b", "8192", "-ub", "8192", "-np", "1", "-ngl", "99", "--alias", "rerank-default",
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"--metrics", "--no-slots", "--no-webui", "--host", "0.0.0.0", "--port", "8082"]
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deploy:
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resources:
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reservations:
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devices: [{driver: nvidia, device_ids: ["${RERANK_GPU:-1}"], capabilities: [gpu]}]
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healthcheck:
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<<: *hc
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test: ["CMD", "curl", "-fsS", "http://127.0.0.1:8082/health"]
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# Qwen3 ohne Denkphase (enable_thinking=false): Verifier und Signatur brauchen kurze, direkte Antworten,
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# und der Canary-Prober fragt mit max_tokens=5 nach "OK".
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llamacpp-chat:
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<<: *llamacpp
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container_name: llamacpp-chat
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profiles: ["chat"]
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command: ["-m", "/models/${CHAT_GGUF:-Qwen3-4B-Q4_K_M.gguf}", "-c", "8192", "-np", "1", "-ngl", "99",
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"--jinja", "--chat-template-kwargs", "{\"enable_thinking\": false}",
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"--alias", "verifier-small", "--metrics", "--no-slots", "--no-webui",
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"--host", "0.0.0.0", "--port", "8080"]
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deploy:
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resources:
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reservations:
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devices: [{driver: nvidia, device_ids: ["${CHAT_GPU:-1}"], capabilities: [gpu]}]
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healthcheck:
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<<: *hc
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test: ["CMD", "curl", "-fsS", "http://127.0.0.1:8080/health"]
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# Startet Container mit Label autoheal=true neu, wenn ihr Healthcheck dauerhaft scheitert
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# (restart: unless-stopped greift nur beim Absturz). Läuft in jedem Profil mit.
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autoheal:
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image: willfarrell/autoheal:1.2.0
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container_name: llamacpp-autoheal
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restart: unless-stopped
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environment: {AUTOHEAL_CONTAINER_LABEL: autoheal}
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volumes: ["/var/run/docker.sock:/var/run/docker.sock"]
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logging: *logging
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# Exporter (node_exporter 9100, nvidia_gpu_exporter 9835) laufen nativ in der VM (Rolle gpu-exporters).
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